Monday, December 6, 2010

eBird animated occurrence maps -- first batch

eBirders,

Spoiler alert: if you like quizzes, and want to figure out this map quiz before seeign a list of potential answers, go to our homepage story before reading on.

We are excited to announce our new "Occurrence Maps" feature, which you can find under the About eBird tab or through links from the new story on the homepage. We have released a few of these in the past, but thanks to a grant from TeraGrid and several years of research, we are finally able to start sharing more of these maps. These maps are really the heart and soul of eBird, since they showcase how your daily eBird submissions are being put to use for science. We are excited about the possibilities that these models hold and will continue to share results and news related to how these are being used.

Starting this week, we will be publishing these maps on the new eBird "Occurrence Maps" feature. Here we provide a short analysis of the patterns you can see, along with an invitation to comment on this blog. If you notice interesting things, have comments on our analysis, or have anything else to share, please post a message below!

This first batch includes ten species, and we will continue to publish five new ones each week. The following species are the ones in our first run:

All of us at eBird think these maps are one of the coolest things we have seen in the bird world in a long time. Not all of them ae perfect, but some are very hard to find any fault with. We are confident that with more eBird data and continued research, these will continue to get more and more accurate.

Again, we invite your comments on these maps so please let us know what you think below!

For all the math and computer science geeks out there: check out "Spatiotemporal exploratory models for broad-scale survey data" by Fink el al. in the current issue of Ecological Applications. It's got the nitty-gritty detail that makes this happen.

These are indeed very cool! And thanks very much for stuff like this that shows how the data is being used. It can sometimes feel like contributing all that data isn't doing anything. But a lighted spot on a map from MY location does a surprisingly good job of alleviating such feelings about my citizen science efforts.

And that A. Pipit map is truly bizarre. If I'm understanding it right, they're coming down in winter but there's little evidence of their return trip up north in the spring? Weird.

I immediately wanted to see a Red-headed woodpecker map - and was very happy to see it included in the initial run.

The map seems to confirm that we still do not know a lot about RHWO movements, specifically its winter range. In the spring and summer the Mississippi Valley and upper midwest light up as it moves northward. In the winter its range contracts southward but this smaller winter range darkens. Should it not light up significantly as RHWO numbers concentrate in a much smaller area?

There's still a lot we do not know about this woodpecker's winter range. I hope someone will take on a project to band and track these woodpeckers.

It is important to remember that these maps are showing OCCURRENCE, not ABUNDANCE. In other words, the maps are displaying the likelihood of encountering a species on a given 1 hour, 1 mile walk in the morning. However, it is not giving any insight as to HOW MANY you might encounter. For this reason, you might not expect the map to "brighten"

Another thing to keep in mind is the scale. Currently this is fixed, but when a bird gets close to the top of the scale, it becomes very difficult to tease out differences. The scale is set to capture a good cross-section of the data, but the very low AND very high occurrence predictions do get lost in this particular visualization.

As in much of the eBird output, this is a visualization of raw data. We firmly believe that these visualizations are very informative, but they do need to be interpreted with caution. The raw data daily predictions for 30k locations) would reveal much more, but is so large that it really needs to be summarized in some useful way such as this.

Adding abundance information is one of the many research directions that we will be taking these models.

Thanks to all for their comments and thanks especially to Zac for pointing out the technical paper that describes STEM. In the coming weeks we will include more maps and more information, so please consider this a first taste.

These models really dampen down outliers...I know for Swainson's Hawk there are a number of more eastern records that aren't seen in the animation. This is something I know you guys may have been working towards, but aren't vagrants sometimes meaningful? I guess in terms of predictive occurrence, not so much.

Also, what role do the boundaries, such as counties, play in drawing of the data, because I see a few artifacts. Ffor example, in fall for Swainson's Hawk, Isle Royale lights up, even though there are no records for Isle Royale, they're coming from reports in the Keweenaw peninsula, with both locations being in the same county.

These maps aren't THE coolest thing I've ever seen, but they're WAY UP THERE on the list. But the BHNU map looks like the species occurred in several southern Indiana counties on May 24, 2008. I'm skeptical. A quick search of IN-Bird-L shows that BHNU was not reported in Indiana that day or any other. And Kenneth Brock's "Birds of Indiana" calls the species "hypothetical" in the state. The maps should show all of Indiana to be absent of BNHU, always. Why does it show them in Indiana?

Tom A: These maps really do dampen outliers (in some cases), and in others they create outliers where they don't exist! (See Brown-headed Nuthatch discussion below). For this reason, we really value the critiques of our 'Chip notes' readers.

In some cases of species at the edges of their ranges, the model is detecting a signal but the scale is set such that it is not showing up on the maps. Note that the Swainson's Hawk scale peaks at 10% (i.e., eastern Colorado) and even in places like eastern Washington, where a regular if low density breeder, it can be hard to detect their occurrence and normal seasonal changes in detectability (or in some cases, birder activity) cause it to disappear. Exploring the eBird frequency in this area reveals low frequencies of 1-5%.

For these models to predict vagrant events (like Swainson's Hawk in the East), it would require a different approach altogether. Try thinking about what your probability is of finding a Swainson's Hawk at most places in the East on a given hour of birding on a one mile walk. Granted, from Sep-Nov it is vastly higher than from Jan-Feb! Probably 0.00001 or lower even at peak seasons. Cape May Point, is, of course, an exception. But even still, when considering the effort (i.e., not a full day on a hawkwatch), it really is low even at Cape May (where Swainson's Hawk is annual in small numbers).

To explore vagrant records, we recommend the standard eBird grid maps or point maps. http://ebird.org/ebird/map/swahaw?neg=true&_neg=on

Dawn: The raw eBird data does not have any Indiana Brown-headed Nuthaches (BHNU), as seen by the grid maps here: http://ebird.org/ebird/map/bnhnut?neg=true&_neg=on (zoom in for close view)

Your question highlights the difference between raw data and models. The model predicts a low incidence of BHNU in southern Indiana and Kentucky, another area where they don;t occur. This is surely due to a combination of 1) seemingly appropriate habitat; 2) sparse data, which forces the model to extrapolate into these regions without enough checklists to train the model that they actually don't occur there.

BHNU is particularly interesting because their range is essentially binary: they either occur, or don't. The model is showing gradation, and is trained to do so because most bird distribution does blur at the edges. In this case, we are certainly not trying to add a bird to the Indiana state list, but we do appreciate you pointing out that the model is overextrapolating in that region.

In interesting question for us will be t try to learn how to teach the model to better show In this case, I would also suggest that most areas with a predicted occurrence that is light orange are areas where BHNU does NOT occur; the ones with solid white define the actual range. One exception is southern Maryland, where BHNU is locally common, but absent from all areas without pines.

Mike D: One of several research questions is how to adapt these maps to look for differences between years.

As the eBird database grows, it will be easier to run these models using single years of data. Now, for them to be good, we need to combine multiple years which immediately make sit impossible to look across years. But certainly, one of our ultimate goals is to use these to develop year to year trends, detect declines etc. The answer until then is please continue to help us grow the database!

Charles S.: We do hope to continue working with the visualization of this data, and one day hope to have a play/pause button on these maps.

Reuven_M: Data from Canada was not included because the remote-sensed variables that we are using come from the US government, including the US Climatic Atlas, the US Census, and NLCD (National Land Cover Dataset). We are currently exploring possibilities for remote-sensed variables that would not be limited by international boundaries, but for the time being, these are not easy to come by an not integrated into our spatial database.

Good question, and we look forward to working with data from Canada (and Mexico, Peru, New Zealand, and Madagascar!) in the future.

This is wonderful! I would love to see data for the Sandhill Cranes. We drive several hours every year to see these giant birds when they winter and court in California's Central Valley. Then they take off for Alaska and even Siberia to raise their young. There is an entirely separate migration path from the Platte River to Canada. I'm sure this would be a great representation.

Great stuff. On RBNU, you are right that this is a case where the model fails in assuming a low density at the periphery--for Louisiana you can be assured that RBNU absolutely does not occur in the Mississippi alluvial valley or the delta--it should show clean, disjunct whitened hot spots west and east of the river with no bleed into the coastal zone.

It is interesting that Willow Flycatcher shows essentially no real predicted detection during spring or fall migration, perhaps not surprising. What then should one make of that odd streak of early detection in Oklahoma-Arkansas?

Nice work! These maps are thought provoking. I was just looking at the maps for the American Pipit. In Texas in autumn and winter, the graph shows really "hot" in the populated corridor from Dallas/Fort Worth, down on through Austin, then San Antonio, and on down to the Valley with hot spots along the highly birded coastal bend near Aransas & Corpus Christie. I wonder if they aren't equally as numerous through out the lesser birded areas in throughout central Oklahoma & Texas.

Hi there - these occurrence maps are breath-taking! I was noting the American Pipit map and saw that posts on spring migration were welcome here. I only recently started posting my observations on eBird, but noted American Pipits consistently and in great numbers all winter long - from about October until mid-February - in two particular fields (I walk past them twice a day with my dogs) here in suburban Pflugerville (outside of Austin.) Then, around February 27th, they, almost literally, disappeared from those fields and I have not seen a single one in almost three days! I will keep watching and posting... regards, HF

The STEM animated maps can be paused (Windows 7 using Chrome or Firefox in my test) by clicking and holding on the window's minimize, maximize, or close buttons. Then to resume, move the mouse pointer off the button and release. Elegant? No. Functional? Yes.